<![CDATA[ML@GT Fall Seminar: Galen Reeves, Duke University]]>

The information-theoretic limits of community detection have been studied extensively for network models with high levels of symmetry or homogeneity. In this talk, Reeves will present a new approach that applies to a broader class of network models that allow for variability in the sizes and behaviors of the different communities, and thus better reflect the behaviors observed in real-world networks. The results show that the ability to detect communities can be described succinctly in terms of a matrix of effective signal-to-noise ratios that provides a geometrical representation of the relationships between the different communities. This characterization follows from a matrix version of the I-MMSE relationship and generalizes the concept of an effective scalar signal-to-noise ratio introduced in previous work.

Galen Reeves joined the faculty at Duke University in Fall 2013, and is currently an Assistant Professor with a joint appointment in the Department of Electrical & Computer Engineering and the Department of Statistical Science. He completed his PhD in Electrical Engineering and Computer Sciences at the University of California, Berkeley in 2011. From 2011 to 2013 he was a postdoctoral associate in the Departments of Statistics at Stanford University, where he was supported by an NSF VIGRE fellowship. In the summer of 2011, he was a postdoctoral researcher in the School of Computer and Communication Sciences at EPFL, Switzerland; in the spring of 2009, he was a visiting scholar at the Technical University of Delft, The Netherlands; and in the summer of 2008, he was a research intern in the Networked Embedded Computing Group at Microsoft Research, Redmond. He received his MS in Electrical Engineering from UC Berkeley in 2007, and BS in Electrical and Computer Engineering from Cornell University in 2005.